Nanofluidic Trapping of Faceted Colloidal Nanocrystals for Parallel Single-Particle Catalysis

Catalyst activity can depend distinctly on nanoparticle size and shape. Therefore, understanding the structure sensitivity of catalytic reactions is of fundamental and technical importance. Experiments with single-particle resolution, where ensemble-averaging is eliminated, are required to study it. Here, we implement the selective trapping of individual spherical, cubic, and octahedral colloidal Au nanocrystals in 100 parallel nanofluidic channels to determine their activity for fluorescein reduction by sodium borohydride using fluorescence microscopy. As the main result, we identify distinct structure sensitivity of the rate-limiting borohydride oxidation step originating from different edge site abundance on the three particle types, as confirmed by first-principles calculations. This advertises nanofluidic reactors for the study of structure–function correlations in catalysis and identifies nanoparticle shape as a key factor in borohydride-mediated catalytic reactions.


Supplementary Text
Figs. S1 to S20 Table S1 Caption for Video S1 Other Supplementary material for this manuscript include the following:

Video S1
Supplementary Text Section 1: Particle trapping in chip with PSQ-bonded lid PSQ is a polymer with siloxane bonds (Si-O-Si) and is known for its excellent aberration resistance which is why it is widely used in coating applications (23,46). PSQ is highly transparent and has a high mechanical modulus. The fact that its chemical structure [RxSiOy]n (where R is a hydrocarbon group) is similar to that of polydimethylsiloxane (PDMS) makes it suitable for micro-and nanofluidic bonding. Gu et.al, established a PSQ bonding process suitable for nanochannels as small as 8 nm in height and this method has further been used in various applications involving micro-and nanofluidic device fabrication (Fig. S1) (24,(47)(48)(49). Here, we use PSQ-bonding of the transparent lids on our nanofluidic chips to enable lid-removal after trapping of colloidal Au nanoparticles to enable SEM imaging of the traps. The process flow of the PSQ-bonding used is depicted in Fig. S1 and the correspondingly obtained result in Fig.  S2. SEM images taken from a large number of particle traps are depicted in Fig. S3. Fig. S4 then summarizes the comparison of trapping analyzed by SEM and DFSM.
Section 2: Measurement and interpretation of fluorescence intensity data Measurements of turnover frequencies (ToFs) were executed with an epi-fluorescence microscope. During measurements, reactant concentrations of 18-40 μM fluorescein and 50 mM sodium borohydride in water were flushed through the nanochannel array with trapped particles, at a constant rate of 495 μm/s for the 32 Au spheres (Fig. 3) or 272 μm/s for the mixed particles (Fig. 4, 5), because of slightly different nanofluidic chip design. The first design contained 3 sets of 100 nanochannels with different traps (vertical constriction, well or horizontal constriction) while the second design contained 3 sets of 100 nanochannels with the preferred trap type used in this work (vertical constriction). The measurements were only done in the nanochannels with the vertical constriction and the difference in flow speed is attributed to the total difference in pressure drop across all nanochannels between the two different chip designs. The flow speed was measured separately for each design by sequentially flushing fluorescein (100 μM) and water through the nanochannels at a flow pressure of 2000 mbar and by evaluating the velocity of the change in brightness. The change in brightness was evaluated by assigning a brightness cutoff which determines if each pixel in the channel is bright or not. The flow speed could then be derived from the change in number of bright pixels between each given frame. By evaluating the speed in both flow directions and taking the mean value from both directions, a flow speed without dependence of brightness cutoff was found (this procedure is also described in our previous study (10)). To verify that the interaction between particle and trap was strong enough to keep the particle in place during the subsequent catalysis experiment, we carried out a "stress test" for each chip by applying a reversed flow of milli-Q water for 20 minutes, while still imaging in DFSM mode. In this way, the eventual detachment of particles was easily detected, enabling the exclusion of corresponding channels from further analysis. During the reversed flow, the geometric structure of the trap "shields" the trapped particles from the flow since they are situated in the recirculation region behind the barrier. In other words, most of the flow through the constriction passes over the particles and thus affects them to much smaller extent than if the constriction would not have been there.
The fluorescein concentration decreased slowly during the experiments from the initial concentration down to 0, as a consequence of a slow background reaction occurring on unspecific sites provided by the large surface area of the microchannels before reaching the nanochannels (by the same principle as in our previous study(10)). To evaluate the intensity downstream of the trapped particles, intensities from two types of reference channels ( ! and " ) were used. ! was recorded in channels that contained multiple nanoparticles and in which, for this reason, the reaction was always in the mass-transport-limited regime throughout the experiments. " was recorded in channels without particles, which therefore showed no activity. Based on these reference intensities and the intensity in each channel ( #$%& ), the normalized intensity ( &'() ) for each channel was calculated according to &'() = . &'() then corresponded to the fraction of non-reduced fluorescein molecules downstream of the nanoparticle for each channel. ToFs were then determined using equation: where is the incoming fluorescein concentration, ,-'. is the volume flowing past the particle per second, and /012/ is the estimated number of surface atoms on the trapped particle in the corresponding nanochannel. For the colloidal nanoparticles /012/ was estimated from the particle mean characteristic length, , (diameter for spheres and side lengths for cube and octahedra) measured with TEM ( Fig. S5, S7). For the different shapes, surface areas were calculated according to /3$2(2 = /3$2(2 " • , #452 = #452 " • 6, and '#1%$26(% = '#1%$26(% " • 2√3 for spheres, cubes and octahedra respectively. The surface areas were then multiplied with the packing factor, divided by the unit cell area, ( 3 ) of the corresponding predominant surface facets (100 for cubes ( 3 = 12.1 nm -2 ), 111 for octahedra( 3 = 13.9 nm -2 ), and an equal mix of 100, 111 and 211 for spheres ( 3 = 11.5 nm -2 )) to arrive at the estimated number of surface atoms /012/ = • 3 . For the calculation of edge sites (Fig. 5C), the total edge lengths ( #452/'#1%$26(% • 12) were divided by the distance between atoms along the edges (0.407 nm for cubes and 0.288 nm for octahedra). Note that since the edge length scales to the power of 1 with side length and the surface area scales with the power of 2, the fraction of edge sites is lower for longer side lengths.
Section 3: Day-to-day variations A general complication in our experiments, and in experimental catalysis in general, is that conditions might vary slightly between measurements, which makes day-to-day experiments difficult to compare directly in a quantitative fashion. To exemplify this, Fig. S10A shows the maximum ToFs for the measurement series in Fig. 3B, while Fig. S10B displays maximum ToFs measured for the exact same 32 particles on three consecutive days, at nominally identical reaction conditions. The mean maximum ToF here varies between 0.2 and 0.9 s -1 for the different experiments. At the same time, the relative activity between the particles was clearly retained between experiments (Fig. S10C, indicating that the particles themselves remained essentially unchanged. This highlights the importance of ensuring identical reaction conditions and simultaneous measurements on differently shaped particles if subtle structure-related effects are to be discovered.
Section 4: Size from dark-field scattering intensity To elucidate a potential influence of particle size on the measured activity traces, we examined the dark-field scattering intensity traces obtained during the particle trapping step (Fig. 2). Specifically, by assuming that the light scattering intensity of each particle, I, is proportional to the particle radius, r, as ∝ 8 , a particle size distribution could be derived from these experiments(50). Normalizing it with the mean particle size obtained from TEM images of particles from the same batch (Fig. S5) revealed a very similar size distribution (Fig. S12A), which meant that we could use the scattering signatures of the individual particles to estimate their size. To emulate a spread in nanoparticle sizes (x-axis in Fig. 5A), the characteristic length for each particle was estimated according to = ( is the intensity step determined with DSFM of the corresponding particle, )2%& is the mean value of all intensity steps and )2%& is mean value of the characteristic length of the particle batch determined from TEM (Fig. S12). The surface area for each particle was then calculated from their individually estimated characteristic length the same way as described in the previous section (Section 2).
Section 5: Simulation of the reaction mechanism with Langmuir-Hinshelwood conditions As the starting point for simulating the reduction of fluorescein as a Langmuir-Hinshelwood reaction, we adopted the same 1D model system that we used to simulate reactivity in a nanochannel in our previous study(10). This system accounts for several 1D channels, each one decorated with four nanoparticles with respective surface area 1 to 4, 16 and 128 (presented as the relative surface area, without unit, since the absolute value is not relevant when we later introduce arbitrary rate constants), and numerically iterates reaction rate, flow and diffusion until a steady state is reached. The reaction properties were then modified to be based on surface coverage ( ) with Langmuir-Hinshelwood conditions defined as where ? are the different surface coverages or free sites ( * ), 0 are the adsorption rate of fluorescein, the adsorption rate of borohydride, the rate of reaction and the desorption rate of the product, respectively, ? is the concentrations of the different reactants and product, ki ± are rate constants, 0 are equilibrium constants, and A, B and AB represent fluorescein, borohydride, and reduced fluorescein, respectively. Initially, all rate constants (ki ± ) were assigned to 1. The rate constants of the reaction between borohydride and fluorescein were then increased to k3 + = 100 and k3 -= 10 to ensure that all fluorescein is consumed in the channels with the largest particles (since that is what we observe experimentally for channels with multiple particles, i.e. larger surface area). Each rate constant was then varied one at a time to explore the effect on the ToF vs. incoming fluorescein concentration, and the conditions at which fluorescein poisoning occurs with a drastic decrease in ToF after reaching max ToF (compare Fig. 3B and Fig. 4B with Fig. S19). As the key result, we then observe a drastic decrease in activity for higher fluorescein concentrations only for low values of either k1or k4 -. This clearly indicates that the low activity for high concentrations of fluorescein is likely to be due to surface poisoning by the fluorescein and/or the reduced fluorescein bound to the catalyst surface. A lower value for k1 - (Fig. S19B-C) results in a higher absorption which increases the activity at lower fluorescein concentrations but lowers the activity at higher fluorescein concentrations due to surface poisoning. A lower value of k4 - (Fig. S19D) has a comparable effect, but mainly the activity at lower fluorescein concentrations is increased since more space is allowed for the fluorescein to bind and the probability of the back reaction is decreased. When both values are lowered, a cooperative effect is observed (Fig. S19E-F). All in all, these simulations corroborate our hypothesis that the decreased activity at higher fluorescein concentration is caused by strong binding (i.e., low desorption rate) of fluorescein to the Au surface and in turn that the poisoning is likely lifted earlier if sites with lower adsorption energy (such as edges) are present. Fig. S4 below. Scale bars are 200 nm.    Fig. 3B. Fig. S11. Average maximal ToF for each particle type across 5 measurement series. Error bars display the standard deviation within each particle type for each series.         Table S1. Zeta-potentials from three measurements for cubes and octahedra stabilized by CTAC, resulting in a positively charged surface, and PVP/Citrate, resulting in a negatively charged surface, clearly showing the success of the ligand exchange from CTAC to PVP. Video S1. Video obtained with DFSM when trapping 100 nm Au spheres in the nanofluidic channels with vertical constrictions.